diff --git a/jaclang_script/FT_script.jac b/jaclang_script/FT_script.jac
new file mode 100644
index 0000000..4087b0f
--- /dev/null
+++ b/jaclang_script/FT_script.jac
@@ -0,0 +1,165 @@
+import:py from unsloth, FastVisionModel ;
+import:py torch ;
+import:py from unsloth, is_bf16_supported ;
+import:py from unsloth.trainer, UnslothVisionDataCollator ;
+import:py from trl, SFTTrainer, SFTConfig ;
+import:py from datasets, load_dataset ;
+import:py from bert_score, score ;
+import:py numpy as np ;
+import:py from transformers, TextStreamer ;
+
+can load_model() {
+
+    (model, tokenizer) = FastVisionModel.from_pretrained(
+        'unsloth/Qwen2-VL-7B-Instruct',
+        load_in_4bit=True,
+        use_gradient_checkpointing='unsloth'
+    );    
+    model = FastVisionModel.get_peft_model(
+        model,
+        finetune_vision_layers=True,
+        finetune_language_layers=True,
+        finetune_attention_modules=True,
+        finetune_mlp_modules=True,
+        r=16,
+        lora_alpha=16,
+        lora_dropout=0,
+        bias='none',
+        random_state=3407,
+        use_rslora=False,
+        loftq_config=None
+    );    
+    return (model, tokenizer) ;
+}
+
+can prep_train_dataset(num_img: Any) {
+
+    instruction = 'Write the LaTeX representation for this image.';    
+    can convert_to_conversation(sample: Any) {
+
+        conversation = [
+            {
+                'role': 'user',
+                'content': [{'type': 'text', 'text': instruction}, {'type': 'image', 'image': sample['image']}]
+            },
+            {
+                'role': 'assistant',
+                'content': [{'type': 'text', 'text': sample['text']}]
+            }
+        ];    
+        return {'messages': conversation} ;
+    }
+    
+    dataset = load_dataset('unsloth/LaTeX_OCR', split='train');    
+    dataset = dataset.select(range(num_img));    
+    converted_dataset = [convert_to_conversation(sample) for sample in dataset];    
+    return converted_dataset ;
+}
+
+can prep_train_model(model: Any, tokenizer: Any, converted_dataset: Any, num_epochs: Any=5) {
+
+    FastVisionModel.for_training(model) ;    
+    trainer = SFTTrainer(
+        model=model,
+        tokenizer=tokenizer,
+        data_collator=UnslothVisionDataCollator(model, tokenizer),
+        train_dataset=converted_dataset,
+        args=SFTConfig(
+            per_device_train_batch_size=2,
+            gradient_accumulation_steps=4,
+            warmup_steps=5,
+            #max_steps=30,
+            num_train_epochs=num_epochs,
+            learning_rate=0.0002,
+            fp16=not is_bf16_supported(),
+            bf16=is_bf16_supported(),
+            logging_steps=1,
+            optim='adamw_8bit',
+            weight_decay=0.01,
+            lr_scheduler_type='linear',
+            seed=3407,
+            output_dir='outputs',
+            report_to='none',
+            remove_unused_columns=False,
+            dataset_text_field='',
+            dataset_kwargs={'skip_prepare_dataset': True},
+            dataset_num_proc=4,
+            max_seq_length=2048
+        )
+    );    
+    return trainer ;
+}
+
+can get_response(test_dataset: Any, model: Any, tokenizer: Any, n: Any) {
+
+    FastVisionModel.for_inference(model) ;    
+    image = test_dataset[n]['image'];    
+    instruction = 'Write the LaTeX representation for this image.';    
+    messages = [
+        {
+            'role': 'user',
+            'content': [{'type': 'image'}, {'type': 'text', 'text': instruction}]
+        }
+    ];    
+    input_text = tokenizer.apply_chat_template(
+        messages,
+        add_generation_prompt=True
+    );    
+    inputs = tokenizer(
+        image,
+        input_text,
+        add_special_tokens=False,
+        return_tensors='pt'
+    ).to('cuda');    
+    text_streamer = TextStreamer(tokenizer, skip_prompt=True);    
+    output_id = model.generate(
+        **inputs,
+        streamer=text_streamer,
+        max_new_tokens=128,
+        use_cache=True,
+        temperature=1.5,
+        min_p=0.1  # use `top_p` instead of `min_p`
+    );
+    response = tokenizer.decode(output_id[0], skip_special_tokens=True);    
+    return response ;
+}
+
+can evaluate(response: Any, reference: Any) {
+
+    (P, R, F1) = score(
+        [response],
+        [reference],
+        model_type='bert-base-uncased',
+        lang='en'
+    );    
+    return {
+        'precision': P.mean().item(),
+        'recall': R.mean().item(),
+        'f1': F1.mean().item()
+    } ;
+}
+
+with entry {
+
+    (model, tokenizer) = load_model();    
+    train_dataset = prep_train_dataset(100);    
+    trainer = prep_train_model(model, tokenizer, train_dataset);    
+    trainer.train() ;         
+    test_dataset = load_dataset('unsloth/LaTeX_OCR', split='test');    
+    response_dict = {'precision': 0, 'recall': 0, 'f1': 0};    
+    
+    for img in range(20) {
+        response = get_response(test_dataset, model, tokenizer, img);    
+        reference = test_dataset[img]['text'];    
+        accuracy = evaluate(response, reference);    
+        response_dict['precision'] += accuracy['precision'];    
+        response_dict['recall'] += accuracy['recall'];    
+        response_dict['f1'] += accuracy['f1'];
+    }
+    
+    precision = (response_dict['precision'] / 20);
+    recall = (response_dict['recall'] / 20);
+    f1 = (response_dict['f1'] / 20);
+      
+    print(f"{'VQA Accuracy: Precision: '}{precision}{', Recall: '}{recall}{', F1: '}{f1}");
+}
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